April 2026
John Ternus' most important move in recent years was leading the Mac transition from x86 (Intel) to ARM (Apple's own Apple Silicon). Pulling off a full-stack shift across hardware, software, and the developer ecosystem in one go, and turning that into a commercial success, required a very high level of execution and tight cross-functional coordination. Without this, there wouldn't be the success of today's MacBook Neo and the advantage Apple now holds as it gears up for AI devices.
The iPhone has been the core driver of Apple's hardware business for nearly two decades, yet the new CEO does not come out of the iPhone side of the business. That suggests the board is applying a broader set of criteria, not just picking the leader with the most visible wins.
Moving the Mac to Apple Silicon was a system- and platform-level transition, essentially a brain transplant. Within Apple, no one has more experience managing a shift at this scale than John Ternus. That is exactly what Apple needs as it moves into the next phase of on-device AI.
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Tim Cook remains one of the few tech leaders able to maintain working relationships with both the U.S. and Chinese governments. Apple will likely still need to rely on him on the geopolitical front in the foreseeable future.
If John Ternus visits Asia, two things to watch: first, the details of his meetings with the Chinese government; second, which Asian suppliers he meets with.
Apple's largest assembly partner, Foxconn, announced on April 1 that Michael Chiang, who leads its iPhone business, will take on the rotating CEO role. At this point, with AI dominating the industry narrative, this move is unlikely to be coincidental. It should help maintain, and potentially strengthen, the relationship with Apple during the CEO transition.
The weird thing right now is the public markets don’t have access to the growth side of software. Right now the trade is to sell SaaS and buy semis (the raw material of AI). What you don’t have yet in the public markets are the AI native software companies and therefore, you’re comparing the practical values of owning say a Salesforce vs the mythical value of owning a company that’s growing 10x (without having seen the actual financials). And everyone is always going to want the myth.
These SaaS stocks aren’t going to trade in a sane fashion until the next generation of AI companies go public and investors can decide how to price a 10% revenue growth company with 30% cash flow vs 300% revenue growth company with negative 100% cash flow and SBC that will blow your mind.
Until then, you’re walking hand in hand with your significant other looking over your shoulder. You know the meme.
— @rodriscoll #
I believe AI will deliver enormous gains to the global consumer: better products, better services, better healthcare, and tools that make ordinary people more capable, even superhuman. The upside is so large, and the geopolitical stakes so real, that we should move decisively toward it, not choke it off.
But people do not experience technological change as an aggregate statistic. They experience it through their bills, their communities, and their jobs.
So the issue is not whether AI will create value. It will. The issue is whether the path to those gains asks particular communities and workers to absorb too much of the cost upfront.
The institutions building AI cannot externalize the local costs of scaling and call future abundance the answer. If datacenters place major new demands on power and land, they should invest enough to strengthen the grid, ease pressure on bills, expand the tax base, and create durable jobs. And if AI compresses some of the entry-level work people used to learn on, firms should help build new on-ramps and training pathways into the new work that growth is creating.
This is not an argument for slowing the buildout down. It is an argument that rapid technological progress has to be socially durable.
OpenAI is quietly turning the Mac Codex app into an all-in-one platform Chat + Codex + OpenClaw, all under one roof.
- Foundation for rendering and reading images + video
- Heartbeat system (like OpenClaw)
- Model and thinking mode selection per task (like an OpenClaw agent manager)
- UI changes to make Codex less "for coders" and more universal
They're using the Codex app as the base and building everything on top of it.
It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and at the same time, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them.